Bulletin of Electrical Engineering and Informatics
Vol 11, No 3: June 2022

Laboratory prediction energy control system based on artificial intelligence network

Desmira Desmira (Universitas Sultan Ageng Tirtayasa)
Norazhar Abu Bakar (Universiti Teknikal Malaysia Melaka)
Mohd Ruzaini Hashim (Universiti Teknikal Malaysia Melaka)
Romi Wiryadinata (Universitas Sultan Ageng Tirtayasa)
Mustofa Abi Hamid (Universitas Sultan Ageng Tirtayasa)



Article Info

Publish Date
01 Jun 2022

Abstract

The use of electrical energy increases globally every year. The laboratory prediction energy control system (LPECS) predicted energy demand. This research was conducted in the Electrical Engineering Vocational Education laboratory by comparing the artificial neural fuzzy system (ANFIS) with the fuzzy logic. The comparison of methods aimed to determine their reliability in the energy demand prediction systems. The results showed that the minimum value of the target data using the conventional method (actual data) was 44.42%. Meanwhile, the prediction data using the ANFIS method was 44.33%, and the prediction data using the fuzzy method was 59.31%. The maximum value of the conventional ways (actual data) of ANFIS and fuzzy was similar by 77.59%. The RMSE ANFIS value was 0.1355%, the mean absolute percentage error (MAPE) was 0.2791%, and the fuzzy logic was 0.1986%. Thus, the ANFIS is applicable to determine the minimum and maximum values. Meanwhile, fuzzy can only show the maximum value but cannot reach the minimum value properly.

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Journal Info

Abbrev

EEI

Publisher

Subject

Electrical & Electronics Engineering

Description

Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the ...